A bipartite graph-based expected networks approach identifies DDR genes not associated with TMB yet predictive of immune checkpoint blockade response

基于二分图的预期网络方法可以识别出与肿瘤突变负荷(TMB)无关但能预测免疫检查点阻断反应的DNA损伤修复(DDR)基因。

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Abstract

Immune checkpoint blockade (ICB) has had remarkable success for treatment of solid tumors. However, as only a subset of patients exhibit responses, there is a continued need for biomarker development. Numerous reports have shown a link between tumor mutational burden (TMB) and ICB response, while others have identified a link between ICB response and mutation in DNA damage repair (DDR) genes. However, it remains unclear to what extent mutations in DDR genes hold predictive value above and beyond their association with TMB. Herein, we present a networks-based test and bipartite graph-based expected TMB score (BiG-BETS) with higher specificity for discriminating DDR genes and pathways that are associated with elevated TMB. Moreover, we find that mutations in certain DDR genes that are not associated with elevated TMB (low BiG-BETS) are nevertheless predictive of ICB benefit in high TMB patients, demonstrating that their inactivation contributes to ICB response in a TMB-independent manner.

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